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. 2023 Jan 1:854:158636.
doi: 10.1016/j.scitotenv.2022.158636. Epub 2022 Sep 7.

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis

Luise Nottmeyer  1 , Ben Armstrong  2 , Rachel Lowe  3 , Sam Abbott  4 , Sophie Meakin  4 , Kathleen M O'Reilly  4 , Rosa von Borries  5 , Rochelle Schneider  6 , Dominic Royé  7 , Masahiro Hashizume  8 , Mathilde Pascal  9 , Aurelio Tobias  10 , Ana Maria Vicedo-Cabrera  11 , Eric Lavigne  12 , Patricia Matus Correa  13 , Nicolás Valdés Ortega  13 , Jan Kynčl  14 , Aleš Urban  15 , Hans Orru  16 , Niilo Ryti  17 , Jouni Jaakkola  17 , Marco Dallavalle  18 , Alexandra Schneider  19 , Yasushi Honda  20 , Chris Fook Sheng Ng  21 , Barrak Alahmad  22 , Gabriel Carrasco-Escobar  23 , Iulian Horia Holobâc  24 , Ho Kim  25 , Whanhee Lee  26 , Carmen Íñiguez  27 , Michelle L Bell  28 , Antonella Zanobetti  22 , Joel Schwartz  22 , Noah Scovronick  29 , Micheline de Sousa Zanotti Stagliorio Coélho  30 , Paulo Hilario Nascimento Saldiva  30 , Magali Hurtado Diaz  31 , Antonio Gasparrini  32 , Francesco Sera  33
Affiliations

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis

Luise Nottmeyer et al. Sci Total Environ. .

Abstract

Background and aim: The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally.

Methods: We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone.

Results: We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries.

Conclusion: Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

Keywords: COVID-19; Distributed lag non-linear modelling; Global analysis; Humidity; Temperature; UV radiation.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
World map showing the included cities colour-coded by region.
Fig. 2
Fig. 2
Time-series of COVID-19 cases per 100,000 inhabitants aggregated by country over the period from 3 February to 31 October 2020.
Fig. 3
Fig. 3
Association between meteorological variables and COVID-19 incidence. Association curves were obtained with meta-regression Model A with random effect defined by country and climatic zones.
Fig. 4
Fig. 4
Country specific association between temperature and COVID-19 incidence. For each country the number (n) of cities included in the analysis is indicated.
Fig. 5
Fig. 5
Country specific association between relative humidity and COVID-19 incidence. For each country the number (n) of cities included in the analysis is indicated.
Fig. 6
Fig. 6
Country specific association between absolute humidity and COVID-19 incidence. For each country the number (n) of cities included in the analysis is indicated.
Fig. 7
Fig. 7
Country specific association between UV radiation and COVID-19 incidence. For each country the number (n) of cities included in the analysis is indicated.
See this image and copyright information in PMC

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